Traditional identification of molds is challenging, cumbersome, and time-consuming, relying on non-specific phenotypic characteristics from culture or expensive and specialized DNA sequencing methods. MALDI-TOF MS has revolutionized clinical microbiology laboratories worldwide, enabling rapid identification of bacteria and yeast. This technology, however, has not been readily applied to mold identification due to technical challenges in developing an efficient protein extraction method and the limited availability of clinically comprehensive databases. Recently, we developed and optimized a simple protein extraction procedure for molds and constructed a highly stringent database comprising 410 individual isolates representing 79 genera and 152 species for the rapid identification of molds by MALDI-TOF MS. The NIH Mold Database is the most comprehensive database developed to date for mold identification and has filled a huge diagnostic gap in the field of clinical microbiology. The NIH Mold Database has now been shared with almost 40 clinical microbiology laboratories worldwide in an effort to improve the diagnosis of invasive fungal infections by providing faster identification (within 30 minutes of growth detection as opposed to weeks) that in turn can guide optimal antifungal therapy. During this fiscal year, an eight-center national controlled study was undertaken to evaluate the clinical performance and accuracy of two fungal databases (the NIH Mold Database and the Bruker Fungi Database) against 80 clinical mold isolates. In addition, a collaborative project was undertaken with another US institution to investigate the effects of different culture conditions (media, time of incubation, solid vs. liquid media) against both fungal databases. Both studies are ongoing and it is envisaged that the findings will highlight important (previously undetermined) factors required for the uniform implementation of MALDI-TOF MS for the routine mold identification in clinical microbiology laboratories worldwide. This in turn will enable faster diagnosis and targeted therapeutic strategies for invasive fungal infections.